Uterine cervical cancer is the second most common cancer in women worldwide, with nearly 500,000 new cases and over 270,000 deaths annually (http://www-depdb.iarc.fr/globocan2002.htm; Ferlay J, Bray F, Pisani P, Parkin D M, eds. Globocan 2000: Cancer incidence, Mortality and Prevalence Worldwide, Version 1.0. IARC CancerBase No. 5. IARC Press; 2001; Pisani P, Parkin D M, Ferlay J. Estimates of the worldwide mortality from eighteen major cancers in 1985. Implications for prevention and projections of future burden. Int J Cancer 1993; 55:891-903). Because invasive disease is preceded by pre-malignant Cervical Intraepithelial Neoplasia (CIN), if detected early and treated adequately, cervical cancer can be universally prevented (Ferris D G, Cox J T, O'Connor D M, Wright V C, Foerster J. Modern Colposcopy, Textbook and Atlas, 2nd Edition. Kendall Hunt, Dubuque, Iowa, 2004). While almost 80% of the cases occur in developing countries where regular screening is unavailable or underutilized (Parkin, D. M., F. I. Bray and S. S. Devesa, Cancer burden in the year 2000. The global picture, Eur J Cancer 37 Suppl 8: S4-66, 2001), there are nearly 15,000 new cases diagnosed and 6,000 deaths annually in the United States (US) and Canada. In the US each year approximately 50 million women undergo cytological screening (Wright, T. C., Jr., J. T. Cox, L. S. Massad, L. B. Twiggs and E. J. Wilkinson, 2001 Consensus Guidelines for the management of women with cervical cytological abnormalities, Jama 287(16): 2120-9, 2001), with some 7% (3.5 million) requiring additional follow-up (Jones, B. A. and D. D. Davey, Quality management in gynecologic cytology using interlaboratory comparison, Arch Pathol Lab Med 124(5): 672-81, 2000; Jones, B. A. and D. A. Novis, Follow-up of abnormal gynecologic cytology: a college of American pathologists Q-probes study of 16132 cases from 306 laboratories, Arch Pathol Lab Med 124(5): 665-71, 2000). It is estimated that the cost for colposcopic follow up and interventional treatment of abnormal cytological screening approaches 6 billion dollars annually in the US (Kurman, R. J., D. E. Henson, A. L. Herbst, K. L. Noller and M. H. Schiffman, Interim guidelines for management of abnormal cervical cytology, The 1992 National Cancer Institute Workshop, Jama 271(23): 1866-9, 1994).
Prophylactic Human Papillomavirus (HPV) vaccines, currently under development, have the potential to prevent cervical cancer. HPV is necessary, but not sufficient alone, for the development of cervical cancer. A monovalent HPV type 16 vaccine has been shown to be both safe and effective in preventing HPV type 16 cervical infections and HPV 16-related cervical cancer precursors (Koutsky L, Ault K A, Wheeler C M, Brown D R, Barr E, Alvarez F B, et al. A controlled trial of a human papillomavirus type 16 vaccine. N Engl J Med 2002; 347:1645-51). Bivalent (HPV types 16 and 18) HPV vaccines could prevent 75% of all cervical cancers (Harper, D. M., E. L. Franco, C. Wheeler, D. G. Ferris, D. Jenkins, A. Schuind, T. Zahaf, B. Innis, P. Naud, N. S. De Carvalho, C. M. Roteli-Martins, J. Teixeira, M. M. Blatter, A. P. Korn, W. Quint, and G. Dubin, Efficacy of a bivalent L1 virus-like particle vaccine in prevention of infection with human papillomavirus types 16 and 18 in young women: a randomised controlled trial. Lancet, 2004. 364(9447): p. 1757-65). However, these vaccines will not be commercially available for at least 3 to 5 years. Further, they will not prevent all cases of cervical cancer. Because vaccination should occur prior to initiating sexual intercourse, it may be 60 years before the risk to various populations will be effectively reduced. The cost of the vaccine will also be substantial, perhaps equivalent to that for Hepatitis B vaccine. The poor and geographically isolated, who are at greatest risk for cervical cancer, may not benefit at all.
Colposcopy is the primary diagnostic method used in the US to detect CIN and cancer, following an abnormal cytological screen (Papanicolaou smear). The purpose of a colposcopic examination is to identify and rank the severity of lesions, so that biopsies representing the highest-grade abnormality can be taken, if necessary. A colposcopic examination involves a systematic visual evaluation of the lower genital tract (cervix, vulva and vagina), with special emphasis on the subjective appearance of metaplastic epithelium comprising the transformation zone on the cervix. For this purpose an optical colposcope is used, which has been in use for almost 80 years. A colposcope is a low powered binocular microscope with a built in white light source and objective lens attached to a support mechanism (B. S. Apgar, Brotzman, G. L. and Spitzer, M., Colposcopy: Principles and Practice, W.B. Saunders Company: Philadelphia, 2002). A green filter may be used to accentuate vasculature. During the exam, a 3-5% acetic acid solution is applied to the cervix, causing abnormal and metaplastic epithelia to turn white. Cervical cancer precursor lesions and invasive cancer exhibit certain distinctly abnormal morphologic features that can be identified by colposcopic examination (Stafl A, Mattingly R F. Colposcopic diagnosis of cervical neoplasia. Obstet Gynecol 1973; 41:168-76; Coppelson M, Dalrymple J C, Atkinson K H. Colposcopic differentiation of abnormalities arising in the transformation zone. Contemp Colposcopy 1993; 20:83-110; Reid R, Krums E P, Herschman B R, et al. Genital warts and cervical cancer V. The tissue basis of colposcopic change. Am J Obstet Gynecol 1984; 149:293-303; Benedet J L, Anderson G H, Boyes D A. Colposcopic diagnosis of invasive and occult carcinoma of the cervix. Obstet Gynecol 1985; 65:557-562). Lesion characteristics such as margin shape; color or opacity; blood vessel caliber, intercapillary spacing and distribution; and contour are considered by physicians (colposcopists) to derive a clinical diagnosis (Reid R, Scalzi P. Genital warts and cervical cancer. VII An improved colposcopic index for differentiating benign papillomaviral infection from high-grade cervical intraepithelial neoplasia. Am J Obstet Gynecol 1985; 153:611-618). These colposcopic signs, when considered aggregately, determine the severity of the neoplasia and discriminate abnormal findings from similarly appearing, anatomically normal variants. Various colposcopic indices, based on grading lesion characteristics, provide clinicians structured approaches to predicting histologic findings (Stafl A, Mattingly R F. Colposcopic diagnosis of cervical neoplasia. Obstet Gynecol 1973; 41:168-76; Coppelson M, Dalrymple J C, Atkinson K H. Colposcopic differentiation of abnormalities arising in the transformation zone. Contemp Colposcopy 1993; 20:83-110; Reid R, Krums E P, Herschman B R, et al. Genital warts and cervical cancer V. The tissue basis of colposcopic change. Am J Obstet Gynecol 1984; 149:293-303). However, due to the subjective nature of the examination, the accuracy of colposcopy is highly dependent upon colposcopist experience and expertise. Even in expert hands, colposcopy suffers from low specificity leading to many unnecessary biopsies (Mikhail, M. S., I. R. Merkatz and S. L. Romney, Clinical usefulness of computerized colposcopy: image analysis and conservative management of mild dysplasia, Obstet Gynecol 80(1): 5-8, 1992). These avoidable biopsies cause an increased risk of infection, patient discomfort, delayed treatment and substantially increased costs.
Digital imaging is revolutionizing medical imaging and enabling sophisticated computer programs to assist the physicians with Computer-Aided-Diagnosis (CAD). Clinicians and academia have suggested and shown proof of concept to use automated image analysis of cervical imagery for cervical cancer screening and diagnosis. In one study, a computer system demonstrated greater agreement rates with histologic diagnoses (85%, k=0.77) than did colposcopists' impressions (66%, k=0.40) (Craine B L, Craine E R. Digital imaging colposcopy; basic concepts in applications. Obstet Gynecol 1993; 82:69-73). In another, the computer system was readily able to discriminate CIN 3 from normal epithelium and immature metaplasia (Eillen D. Dickman, Theodore J. Doll, Chun Kit Chiu, and Daron G. Ferris, Identification of Cervical Neoplasia Using a Simulation of Human Vision, Journal of Lower Genital Tract Disease, Vol. 5, No. 3, 2001, pp 144-152). One computer system for colposcopy has also demonstrated an ability to serially monitor untreated low grade lesions for evidence of progression or regression (Mikhail M S, Merkatz I R, Rommey S L. Clinical usefulness of computerized colposcopy: Image analysis and conservative management of mild dysplasia. Obstet Gynecol 1992; 80:5-8). Since intercapillary distances increase proportionally with disease severity, another computer system was able to measure these tiny distances to successfully predict the specific level of cervical neoplasia (Mikhail M S, Romney S L. Computerized measurement of intercapillary distance using image analysis in women with cervical intraepithelial neoplasia: correlation with severity. Obstet Gynecol 2000; 95:52-3).
Various image processing algorithms have been developed to detect different colposcopic features. At the University of New South Wales (UNSW), Australia, Van Raad developed algorithms to detect the transformation zone using an active contours model (snakes) at multiple scales (Viara Van Raad, Active Contour Models—A Multiscale Implementation for Anatomical Feature Delineation in Cervical Images; Proceedings of the IEEE International Conference of Image Processing—ICIP 2004, Oct. 24-27, pp. 557-560, 2004; Van Raad, V. and Bradley A.; Active contour model based segmentation of colposcopy images from cervix uteri using Gaussian Pyramids; Proceedings of the 6th International Symposium on DSP and Communication Systems. 133-138, January 2002) and a novel wavelet-based algorithm looking at local frequency content (V. Van Raad; A Novel Wavelet-based Image Analysis Algorithm for Detection of Important Anatomical Features in Colposcopy Image; Proceedings of ICBME'02. 61-62, December 2002). Yang et al., at Texas Tech University, developed a segmentation algorithm to detect acetowhite epithelium using a statistical optimization scheme (deterministic annealing) for accurate clustering to track the boundaries of the acetowhite regions (Yang S., Guo J., King P., Sriraja Y., Mitra S., Nutter B., Ferris D., Schiffman M., Jeronimo J., and Long R.; A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer; Proceedings of SPIE on Medical Imaging, Vol. 5370 Bellingham, Wash. 2004, pages 1833-1844). Gordon and coworkers, at Tel-Aviv University, developed a segmentation algorithm for three tissue types in cervical imagery (original squamous, columnar, and acetowhite epithelium) based on color and texture information (Gordon S., Zimmerman G., and Greenspan H.; Image segmentation of Uterine Cervix images for indexing in PACS; Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems (CBMS'04), 2004). The set of regions in the images was represented by a Gaussian mixture model, while an Expectation-Maximization algorithm was used to determine the maximum likelihood parameters of the statistical model in the feature space. As a result, the labeling of a pixel could be affiliated with the most probable Gaussian cluster according to Bayes rule. Ji et al (Qiang Ji, John Engel, and Eric Craine, Texture Analysis for classification of Cervix Lesions, IEEE Transactions on Medical Imaging, Vol. 19, No. 11, November 2000, pp 1144-1149; Qiang Ji, John Engel, and Eric Craine, Classifying cervix tissue patterns with texture analysis, Pattern Recognition, Vol. 33, 1561-1573) presented a generalized texture analysis algorithm for classifying the vascular patterns from colposcopic images. They investigated six characteristic pathological vascular patterns, including network capillaries, hairpin capillaries, two types of punctation vessels and two types of mosaic vessels. Others have applied a combination of conventional statistical and structural texture analysis approaches. For example, Balas (Costas Balas, A novel optical imaging method for the early detection, quantitative grading, and mapping of cancerous and precancerous lesions of cervix, IEEE Transactions on Biomedical Engineering, Vol. 48, No. 1, January 2001, 96-104) and Orfanoudaki et al. (Irene M. Orfanoudaki, G. C. Themelis, S. K. Sifakis, D. H. Fragouli, J. G. Panayiotides, E. M. Vazgiouraki, E. E. Koumantakis, A clinical study of optical biopsy of the uterine cervix using a multispectral imaging system, Gynecologic Oncology, Vol. 96, 2005, 119-131) analyzed the temporal decay of the acetic acid whitening effect by measuring the intensity profile over time. Furthermore, several approaches for tissue classification have been developed: a simple colposcopic image classification method by artificial neural network using the lesion contour features (I. Claude, R. Winzenrieth, P. Pouletaut, and J. C. Boulanger, Contour Features for colposcopic image classification by artificial neural networks, in Proceedings of international conference on Pattern Recognition, 2002, 771-774), a rule based medical decision support system for detecting different stages of cervical cancer based on the signs and symptoms from physical examination (Pabitra Mitra, Sushmita Mitra, and Sankar K. Pal, Staging of Cervical Cancer with Soft Computing, IEEE Transactions on Biomedical Engineering, Vol. 47, No. 7, July 2000, pp 934-940), the classification of cervical tissue based on spectral data using multi-layered perceptrons and Radial Basis Function (RBF) networks (Kagan Tumer, Nirmala Ramanujam, Joydeep Ghosh, and Rebecca Richards-Kortum, Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Precancer, IEEE Transactions on Biomedical Engineering, Vol. 45, No. 8, August 1998) and multivariate stochastic training algorithms (A. K. Dattamajumdar, D. Wells, J. Parnell, J. T. Lewis, D. Ganguly and T. C. Wright Jr., Preliminary experimental results from multi-center clinical trials for detection of cervical precancerous lesions using the Cerviscan™ system: a novel full field evoked tissue fluorescence based imaging instrument, in Proceedings of the 23rd Annual EMBS international conference, October 25-28, Istanbul, Turkey, pp 3150-3152).
CAD for colposcopy could have a direct impact on improving women's health care and reducing associated costs. A product realization where a CAD system is incorporated into a low-cost hand-held device, creating in effect a machine expert colposcopist, could improve screening cost-effectiveness in developing countries. Similarly, a product realization, where a CAD system operates as an adjunct to colposcopy could minimize the high variability among colposcopists and establish a consistent, higher standard for accuracy. Consequently, fewer false-positive biopsies or ultimately no biopsies would be required.